Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

Garg, Neha; Kapono, Clifford A.; Lim, Yan Wei; Koyama, Nobuhiro; Vermeij, Mark J. A.; Conrad, Douglas; Rohwer, Forest; Dorrestein, Pieter C.

doi:10.1016/j.ijms.2014.06.005

Cited by 85 publications

(74 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, imaging mass spectrometry can be especially valuable in direct molecular analysis of gut tissue sections (Rath et al, 2012), enabling spatial investigation of host-microbiome interactions. Molecular networking is an effective strategy for differentiating metabolites of microbial and human origin (Garg et al, 2014) from the complex environment of a CF-associated human lung. Since most specialized metabolites have associated biosynthetic pathways that are unique to an organism or a class of organisms, a combination of metagenome and metatranscriptome sequencing (Quinn et al, 2014), and high resolution mass spectrometry-based molecular networking analysis combined with statistical approaches will allow the investigation of specialized metabolite associated crosstalk between a microbiome and its host.…”

Section: Specialized Metabolites From Microbial Isolatesmentioning

confidence: 99%

Specialized Metabolites from the Microbiome in Health and Disease

et al. 2014

Self Cite

View full text Add to dashboard Cite

The microbiota, and the genes that comprise its microbiome, play key roles in human health. Host-microbe interactions affect immunity, metabolism, development, and behavior, and dysbiosis of gut bacteria contributes to disease. Despite advances in correlating changes in the microbiota with various conditions, specific mechanisms of host-microbiota signaling remain largely elusive. We discuss the synthesis of microbial metabolites, their absorption, and potential physiological effects on the host. We propose that the effects of specialized metabolites may explain present knowledge gaps linking the gut microbiota to biological host mechanisms during initial colonization, and in health and disease.

show abstract

Section: Specialized Metabolites From Microbial Isolatesmentioning

confidence: 99%

Specialized Metabolites from the Microbiome in Health and Disease

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, while some metabolomes (e.g. the human metabolome) have been widely characterized and are accessible in databases such as: Metlin 149 , Massbank 150 , LipidBlast 151 , HMDB 152 , NIST 153 , ReSpect 104, 1, 154, 155 (Table 2), complementary microbial metabolome databases are now emerging for a few model species 156, 157 . But the vast majority of microbial metabolomes are missing.…”

Section: Studying the Microbial Metabolome With Mass Spectrometry mentioning

confidence: 99%

Mass spectrometry tools and workflows for revealing microbial chemistry

Luzzatto-Knaan

Melnik

Dorrestein

2015

Analyst

Self Cite

View full text Add to dashboard Cite

Since the time Van Leeuwenhoek was able to observe microbes through a microscope, an innovation that led to the birth of the field of microbiology, we have aimed to understand how microorganisms function, interact and communicate. The exciting progress in the development of analytical technologies and workflows has demonstrated mass spectrometry as very powerful technique for the interrogation of microbiology at the molecular level. In this review, we aim to highlight the available and emerging tools in mass spectrometry for microbial analysis by overviewing the methods and workflow advances for taxonomic identification, microbial interaction, dereplication and drug discovery. We emphasize their potential for future development, point out unsolved problems and future directions that would aid in the analysis of the chemistry produced by microbes.

show abstract

“…The paucity of techniques that share information across fragmentation spectra can be explained by the complexity of fragmentation data (14). One example, "Molecular Networking," clusters MS1 peaks by their MS2 spectral similarity such that one structurally annotated metabolite in a cluster facilitates structural annotation of its neighbors (15,16).…”

mentioning

confidence: 99%

Topic modeling for untargeted substructure exploration in metabolomics

Hooft

Wandy

Barrett

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

281

265

View full text Add to dashboard Cite

The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.metabolomics | mass spectrometry | fragmentation | bioinformatics | topic modeling M ass spectrometry (MS)-based metabolomics aims to capture the entire small-molecule composition of biological systems. Analysis of MS metabolomics data are challenging as many molecules cannot be identified from their mass (e.g., isobaric molecules, and isomers) (1-3). Separation by liquid chromatography before MS (LC-MS) can add discriminatory information but does not solve the problem as isomers can exhibit similar chromatographic behavior, and chromatographic retention time is currently unpredictable.Fragmentation spectra have been used to partially overcome this problem (4-6). Most tools compare individual fragmentation spectra to reference spectra (5, 7) stored in public databases, for example, MassBank (8) or Human Metabolome Database (9), and are thus constrained by the limited number of reference spectra (10-12). Poor identification coverage can result in poor biochemical insight. We propose a method that analyzes all acquired fragmentation spectra to expose underlying biochemistry without relying on metabolite identification, inspired by machine-learning techniques developed initially for text processing (13).The paucity of techniques that share information across fragmentation spectra can be explained by the complexity of fragmentation data (14). One example, "Molecular Networking," clusters MS1 peaks by th...

show abstract

Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

Cited by 85 publications

References 47 publications

Specialized Metabolites from the Microbiome in Health and Disease

Specialized Metabolites from the Microbiome in Health and Disease

Mass spectrometry tools and workflows for revealing microbial chemistry

Topic modeling for untargeted substructure exploration in metabolomics

Contact Info

Product

Resources

About